Overview

Dataset statistics

Number of variables11
Number of observations26298
Missing cells24503
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory88.0 B

Variable types

Categorical1
Numeric10

Alerts

time has a high cardinality: 26298 distinct values High cardinality
tavg is highly correlated with tmin and 3 other fieldsHigh correlation
tmin is highly correlated with tavg and 2 other fieldsHigh correlation
tmax is highly correlated with tavg and 1 other fieldsHigh correlation
wspd is highly correlated with wpgtHigh correlation
wpgt is highly correlated with wspdHigh correlation
tsun is highly correlated with tavgHigh correlation
snow is highly correlated with tavg and 1 other fieldsHigh correlation
snow has 1346 (5.1%) missing values Missing
wdir has 9229 (35.1%) missing values Missing
wspd has 6956 (26.5%) missing values Missing
wpgt has 6949 (26.4%) missing values Missing
time is uniformly distributed Uniform
time has unique values Unique
tmin has 359 (1.4%) zeros Zeros
prcp has 14627 (55.6%) zeros Zeros
snow has 23661 (90.0%) zeros Zeros
tsun has 4980 (18.9%) zeros Zeros

Reproduction

Analysis started2022-11-24 15:55:29.542286
Analysis finished2022-11-24 15:57:51.900260
Duration2 minutes and 22.36 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

time
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct26298
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size205.6 KiB
1950-01-01
 
1
1997-12-27
 
1
1998-01-06
 
1
1998-01-05
 
1
1998-01-04
 
1
Other values (26293)
26293 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters262980
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26298 ?
Unique (%)100.0%

Sample

1st row1950-01-01
2nd row1950-01-02
3rd row1950-01-03
4th row1950-01-04
5th row1950-01-05

Common Values

ValueCountFrequency (%)
1950-01-011
 
< 0.1%
1997-12-271
 
< 0.1%
1998-01-061
 
< 0.1%
1998-01-051
 
< 0.1%
1998-01-041
 
< 0.1%
1998-01-031
 
< 0.1%
1998-01-021
 
< 0.1%
1998-01-011
 
< 0.1%
1997-12-311
 
< 0.1%
1997-12-301
 
< 0.1%
Other values (26288)26288
> 99.9%

Length

2022-11-24T16:57:52.441266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1950-01-011
 
< 0.1%
1950-01-171
 
< 0.1%
1950-01-071
 
< 0.1%
1950-01-081
 
< 0.1%
1950-01-091
 
< 0.1%
1950-01-101
 
< 0.1%
1950-01-111
 
< 0.1%
1950-01-121
 
< 0.1%
1950-02-191
 
< 0.1%
1950-01-131
 
< 0.1%
Other values (26288)26288
> 99.9%

Most occurring characters

ValueCountFrequency (%)
-52596
20.0%
147586
18.1%
046815
17.8%
929167
11.1%
226770
10.2%
811035
 
4.2%
511031
 
4.2%
711031
 
4.2%
610964
 
4.2%
38675
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number210384
80.0%
Dash Punctuation52596
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147586
22.6%
046815
22.3%
929167
13.9%
226770
12.7%
811035
 
5.2%
511031
 
5.2%
711031
 
5.2%
610964
 
5.2%
38675
 
4.1%
47310
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
-52596
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common262980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-52596
20.0%
147586
18.1%
046815
17.8%
929167
11.1%
226770
10.2%
811035
 
4.2%
511031
 
4.2%
711031
 
4.2%
610964
 
4.2%
38675
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII262980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-52596
20.0%
147586
18.1%
046815
17.8%
929167
11.1%
226770
10.2%
811035
 
4.2%
511031
 
4.2%
711031
 
4.2%
610964
 
4.2%
38675
 
3.3%

tavg
Real number (ℝ)

HIGH CORRELATION

Distinct430
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.71841965
Minimum-17.6
Maximum31.6
Zeros92
Zeros (%)0.3%
Negative1975
Negative (%)7.5%
Memory size205.6 KiB
2022-11-24T16:57:53.317973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-17.6
5-th percentile-1.4
Q15
median10.8
Q316.7
95-th percentile22.4
Maximum31.6
Range49.2
Interquartile range (IQR)11.7

Descriptive statistics

Standard deviation7.52784232
Coefficient of variation (CV)0.7023276345
Kurtosis-0.6424771782
Mean10.71841965
Median Absolute Deviation (MAD)5.9
Skewness-0.1210460448
Sum281873
Variance56.66840999
MonotonicityNot monotonic
2022-11-24T16:57:54.770685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5160
 
0.6%
13157
 
0.6%
4.4157
 
0.6%
6.8156
 
0.6%
14.8153
 
0.6%
5.8151
 
0.6%
15.4151
 
0.6%
17.4151
 
0.6%
16.6150
 
0.6%
7.4148
 
0.6%
Other values (420)24764
94.2%
ValueCountFrequency (%)
-17.61
< 0.1%
-14.41
< 0.1%
-141
< 0.1%
-13.71
< 0.1%
-13.61
< 0.1%
-13.51
< 0.1%
-13.41
< 0.1%
-13.11
< 0.1%
-12.81
< 0.1%
-12.71
< 0.1%
ValueCountFrequency (%)
31.61
< 0.1%
30.91
< 0.1%
30.71
< 0.1%
30.21
< 0.1%
29.91
< 0.1%
29.82
< 0.1%
29.62
< 0.1%
29.51
< 0.1%
29.41
< 0.1%
29.31
< 0.1%

tmin
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct403
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.244539509
Minimum-21.1
Maximum22.3
Zeros359
Zeros (%)1.4%
Negative4647
Negative (%)17.7%
Memory size205.6 KiB
2022-11-24T16:57:55.872348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-21.1
5-th percentile-4.6
Q11.3
median6.4
Q311.6
95-th percentile16.4
Maximum22.3
Range43.4
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation6.682959394
Coefficient of variation (CV)1.070208521
Kurtosis-0.3302180031
Mean6.244539509
Median Absolute Deviation (MAD)5.2
Skewness-0.2794648095
Sum164218.9
Variance44.66194626
MonotonicityNot monotonic
2022-11-24T16:57:57.040478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0359
 
1.4%
13180
 
0.7%
5174
 
0.7%
0.4159
 
0.6%
4.8159
 
0.6%
1.6158
 
0.6%
6155
 
0.6%
6.4154
 
0.6%
1153
 
0.6%
3152
 
0.6%
Other values (393)24495
93.1%
ValueCountFrequency (%)
-21.11
< 0.1%
-211
< 0.1%
-20.51
< 0.1%
-19.31
< 0.1%
-19.22
< 0.1%
-18.72
< 0.1%
-18.62
< 0.1%
-18.32
< 0.1%
-181
< 0.1%
-17.71
< 0.1%
ValueCountFrequency (%)
22.32
 
< 0.1%
22.21
 
< 0.1%
22.12
 
< 0.1%
21.91
 
< 0.1%
21.81
 
< 0.1%
21.71
 
< 0.1%
21.62
 
< 0.1%
21.51
 
< 0.1%
21.42
 
< 0.1%
21.25
< 0.1%

tmax
Real number (ℝ)

HIGH CORRELATION

Distinct476
Distinct (%)1.8%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.33199041
Minimum-13
Maximum70
Zeros92
Zeros (%)0.3%
Negative791
Negative (%)3.0%
Memory size205.6 KiB
2022-11-24T16:57:58.291571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile1.2
Q18.4
median15.5
Q322.3
95-th percentile29.3
Maximum70
Range83
Interquartile range (IQR)13.9

Descriptive statistics

Standard deviation8.86628251
Coefficient of variation (CV)0.5782864632
Kurtosis-0.5912898123
Mean15.33199041
Median Absolute Deviation (MAD)6.9
Skewness0.001366055943
Sum403016.7
Variance78.61096554
MonotonicityNot monotonic
2022-11-24T16:57:59.191164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10133
 
0.5%
21133
 
0.5%
21.3129
 
0.5%
21.2124
 
0.5%
9122
 
0.5%
11.6121
 
0.5%
11120
 
0.5%
19.7117
 
0.4%
22.7117
 
0.4%
18.8116
 
0.4%
Other values (466)25054
95.3%
ValueCountFrequency (%)
-131
 
< 0.1%
-10.62
< 0.1%
-10.41
 
< 0.1%
-102
< 0.1%
-9.51
 
< 0.1%
-91
 
< 0.1%
-8.91
 
< 0.1%
-8.82
< 0.1%
-8.73
< 0.1%
-8.43
< 0.1%
ValueCountFrequency (%)
704
< 0.1%
39.81
 
< 0.1%
39.51
 
< 0.1%
391
 
< 0.1%
38.92
< 0.1%
38.81
 
< 0.1%
38.51
 
< 0.1%
38.32
< 0.1%
38.11
 
< 0.1%
381
 
< 0.1%

prcp
Real number (ℝ≥0)

ZEROS

Distinct321
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.707517682
Minimum0
Maximum103.1
Zeros14627
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size205.6 KiB
2022-11-24T16:58:00.903840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile9
Maximum103.1
Range103.1
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation3.998482827
Coefficient of variation (CV)2.341693365
Kurtosis47.45184898
Mean1.707517682
Median Absolute Deviation (MAD)0
Skewness5.038111356
Sum44904.3
Variance15.98786492
MonotonicityNot monotonic
2022-11-24T16:58:02.041712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014627
55.6%
0.1937
 
3.6%
0.2624
 
2.4%
0.3511
 
1.9%
0.5379
 
1.4%
0.4371
 
1.4%
0.6309
 
1.2%
0.7285
 
1.1%
0.8276
 
1.0%
1221
 
0.8%
Other values (311)7758
29.5%
ValueCountFrequency (%)
014627
55.6%
0.1937
 
3.6%
0.2624
 
2.4%
0.3511
 
1.9%
0.4371
 
1.4%
0.5379
 
1.4%
0.6309
 
1.2%
0.7285
 
1.1%
0.8276
 
1.0%
0.9217
 
0.8%
ValueCountFrequency (%)
103.11
< 0.1%
721
< 0.1%
631
< 0.1%
57.71
< 0.1%
56.71
< 0.1%
51.51
< 0.1%
49.21
< 0.1%
49.12
< 0.1%
48.11
< 0.1%
47.31
< 0.1%

snow
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct28
Distinct (%)0.1%
Missing1346
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean2.794565566
Minimum0
Maximum300
Zeros23661
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size205.6 KiB
2022-11-24T16:58:02.922428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum300
Range300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.28499899
Coefficient of variation (CV)5.827381253
Kurtosis75.91924736
Mean2.794565566
Median Absolute Deviation (MAD)0
Skewness7.948278888
Sum69730
Variance265.2011921
MonotonicityNot monotonic
2022-11-24T16:58:04.519981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
023661
90.0%
10285
 
1.1%
20223
 
0.8%
30143
 
0.5%
4079
 
0.3%
5074
 
0.3%
10067
 
0.3%
8066
 
0.3%
6062
 
0.2%
7058
 
0.2%
Other values (18)234
 
0.9%
(Missing)1346
 
5.1%
ValueCountFrequency (%)
023661
90.0%
10285
 
1.1%
20223
 
0.8%
30143
 
0.5%
4079
 
0.3%
5074
 
0.3%
6062
 
0.2%
7058
 
0.2%
8066
 
0.3%
9055
 
0.2%
ValueCountFrequency (%)
3001
 
< 0.1%
2604
< 0.1%
2501
 
< 0.1%
2403
 
< 0.1%
2302
 
< 0.1%
2202
 
< 0.1%
2104
< 0.1%
2006
< 0.1%
1905
< 0.1%
1809
< 0.1%

wdir
Real number (ℝ≥0)

MISSING

Distinct361
Distinct (%)2.1%
Missing9229
Missing (%)35.1%
Infinite0
Infinite (%)0.0%
Mean184.5266858
Minimum0
Maximum360
Zeros90
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size205.6 KiB
2022-11-24T16:58:06.651829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1114
median191
Q3249
95-th percentile349
Maximum360
Range360
Interquartile range (IQR)135

Descriptive statistics

Standard deviation103.1560859
Coefficient of variation (CV)0.5590307193
Kurtosis-0.8412103719
Mean184.5266858
Median Absolute Deviation (MAD)65
Skewness-0.1596919697
Sum3149686
Variance10641.17806
MonotonicityNot monotonic
2022-11-24T16:58:10.844340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187133
 
0.5%
172130
 
0.5%
192129
 
0.5%
191127
 
0.5%
183121
 
0.5%
182120
 
0.5%
189120
 
0.5%
190118
 
0.4%
184116
 
0.4%
197114
 
0.4%
Other values (351)15841
60.2%
(Missing)9229
35.1%
ValueCountFrequency (%)
090
0.3%
190
0.3%
290
0.3%
374
0.3%
480
0.3%
594
0.4%
676
0.3%
765
0.2%
873
0.3%
985
0.3%
ValueCountFrequency (%)
3605
 
< 0.1%
35982
0.3%
35881
0.3%
35780
0.3%
35687
0.3%
35567
0.3%
35490
0.3%
35379
0.3%
35279
0.3%
35168
0.3%

wspd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct100
Distinct (%)0.5%
Missing6956
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean9.7010547
Minimum0
Maximum37.8
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size205.6 KiB
2022-11-24T16:58:12.775222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6
Q16.1
median9
Q312.2
95-th percentile18.4
Maximum37.8
Range37.8
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation4.633108409
Coefficient of variation (CV)0.4775881131
Kurtosis1.148992072
Mean9.7010547
Median Absolute Deviation (MAD)2.9
Skewness0.9354449428
Sum187637.8
Variance21.46569353
MonotonicityNot monotonic
2022-11-24T16:58:14.055767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6691
 
2.6%
6.8686
 
2.6%
8.3679
 
2.6%
7.2675
 
2.6%
6.1668
 
2.5%
5.8668
 
2.5%
6.5660
 
2.5%
7.9653
 
2.5%
5.4635
 
2.4%
9624
 
2.4%
Other values (90)12703
48.3%
(Missing)6956
26.5%
ValueCountFrequency (%)
05
 
< 0.1%
0.419
 
0.1%
0.715
 
0.1%
1.131
 
0.1%
1.444
 
0.2%
1.862
 
0.2%
2.289
 
0.3%
2.5135
0.5%
2.9157
0.6%
3.2250
1.0%
ValueCountFrequency (%)
37.81
 
< 0.1%
36.71
 
< 0.1%
34.92
 
< 0.1%
32.41
 
< 0.1%
313
< 0.1%
30.25
< 0.1%
29.95
< 0.1%
29.54
< 0.1%
29.25
< 0.1%
28.84
< 0.1%

wpgt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct266
Distinct (%)1.4%
Missing6949
Missing (%)26.4%
Infinite0
Infinite (%)0.0%
Mean30.82724689
Minimum2.9
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size205.6 KiB
2022-11-24T16:58:15.265377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile12.6
Q121.6
median28.8
Q338.2
95-th percentile55.4
Maximum130
Range127.1
Interquartile range (IQR)16.6

Descriptive statistics

Standard deviation13.37380902
Coefficient of variation (CV)0.4338307947
Kurtosis1.730543295
Mean30.82724689
Median Absolute Deviation (MAD)8.3
Skewness0.9728546926
Sum596476.4
Variance178.8587676
MonotonicityNot monotonic
2022-11-24T16:58:16.877216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.2373
 
1.4%
18330
 
1.3%
21.6328
 
1.2%
28.8321
 
1.2%
32.4286
 
1.1%
23.4246
 
0.9%
25.9245
 
0.9%
22.3244
 
0.9%
25.6242
 
0.9%
28.1240
 
0.9%
Other values (256)16494
62.7%
(Missing)6949
26.4%
ValueCountFrequency (%)
2.91
 
< 0.1%
3.61
 
< 0.1%
42
 
< 0.1%
4.32
 
< 0.1%
51
 
< 0.1%
5.48
< 0.1%
5.84
 
< 0.1%
6.16
< 0.1%
6.511
< 0.1%
6.88
< 0.1%
ValueCountFrequency (%)
1301
< 0.1%
1171
< 0.1%
114.51
< 0.1%
1121
< 0.1%
103.31
< 0.1%
102.21
< 0.1%
101.51
< 0.1%
100.81
< 0.1%
100.41
< 0.1%
98.31
< 0.1%

pres
Real number (ℝ≥0)

Distinct586
Distinct (%)2.2%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1016.208048
Minimum970.8
Maximum1046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size205.6 KiB
2022-11-24T16:58:18.966749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum970.8
5-th percentile1001.7
Q11011.1
median1016.3
Q31021.6
95-th percentile1030.2
Maximum1046
Range75.2
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation8.512573648
Coefficient of variation (CV)0.008376802036
Kurtosis0.5405641174
Mean1016.208048
Median Absolute Deviation (MAD)5.2
Skewness-0.1879056471
Sum26717125.8
Variance72.46391012
MonotonicityNot monotonic
2022-11-24T16:58:21.506169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014.9163
 
0.6%
1015.2159
 
0.6%
1014.8157
 
0.6%
1017.2150
 
0.6%
1015.9149
 
0.6%
1016.7148
 
0.6%
1017147
 
0.6%
1017.8147
 
0.6%
1016.4147
 
0.6%
1015.7146
 
0.6%
Other values (576)24778
94.2%
ValueCountFrequency (%)
970.81
< 0.1%
972.11
< 0.1%
972.71
< 0.1%
977.91
< 0.1%
978.51
< 0.1%
979.91
< 0.1%
9811
< 0.1%
981.21
< 0.1%
981.61
< 0.1%
982.21
< 0.1%
ValueCountFrequency (%)
10461
< 0.1%
1045.52
< 0.1%
1044.51
< 0.1%
1043.91
< 0.1%
1043.81
< 0.1%
1043.41
< 0.1%
1043.21
< 0.1%
10431
< 0.1%
1042.91
< 0.1%
1042.61
< 0.1%

tsun
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct586
Distinct (%)2.2%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean283.8643797
Minimum0
Maximum948
Zeros4980
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size205.6 KiB
2022-11-24T16:58:24.291764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median222
Q3492
95-th percentile774
Maximum948
Range948
Interquartile range (IQR)468

Descriptive statistics

Standard deviation264.0536307
Coefficient of variation (CV)0.9302105144
Kurtosis-0.900824564
Mean283.8643797
Median Absolute Deviation (MAD)216
Skewness0.5802123319
Sum7463930
Variance69724.31987
MonotonicityNot monotonic
2022-11-24T16:58:25.703823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04980
 
18.9%
6528
 
2.0%
12426
 
1.6%
18357
 
1.4%
24305
 
1.2%
30283
 
1.1%
36261
 
1.0%
48250
 
1.0%
42241
 
0.9%
60235
 
0.9%
Other values (576)18428
70.1%
ValueCountFrequency (%)
04980
18.9%
14
 
< 0.1%
210
 
< 0.1%
37
 
< 0.1%
44
 
< 0.1%
54
 
< 0.1%
6528
 
2.0%
74
 
< 0.1%
84
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
9481
 
< 0.1%
9421
 
< 0.1%
9364
 
< 0.1%
9304
 
< 0.1%
9292
 
< 0.1%
9281
 
< 0.1%
92410
< 0.1%
9221
 
< 0.1%
9211
 
< 0.1%
9201
 
< 0.1%

Interactions

2022-11-24T16:57:35.759976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:17.103098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:25.532915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:33.335466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:42.041172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:48.951598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:58.556053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:07.523217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:17.163182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:26.547815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:36.541539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:18.655188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:26.104931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:34.122530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:42.972914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:49.544549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:59.353444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:08.181810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:18.003288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:27.301347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:37.738895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:19.587415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:26.684951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:35.087983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:43.939188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:50.189791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:59.972415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:08.961739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:18.933448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:28.572083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:38.556431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:20.285125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:27.323256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:35.741013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:44.604980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:50.871965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:00.924913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:09.822777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:19.922225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:29.275204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:39.480508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:20.850826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:28.917144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:36.502492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:45.069281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:51.973387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:01.985890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:10.684975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:20.938131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:30.222361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:40.689214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:21.659551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:29.540946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:37.468454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:45.655732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:52.799697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:02.944964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:11.522465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:21.778424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:30.948416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:41.554721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:22.605951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:30.522347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:38.223419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:46.192872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:54.018869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:04.151697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:12.308793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:22.748505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:31.986735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:43.210733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:23.386979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:31.172585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:38.935123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:46.868468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:55.358439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:05.019261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:12.973669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:23.585313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:32.971447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:44.177099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:24.306472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:32.033067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:40.136483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:47.391313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:56.720401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:05.847685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:14.224449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:24.734702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:33.774800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:45.112930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:25.036209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:32.700282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:41.091109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:48.308137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:56:57.523305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:06.705865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:15.416717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:25.696713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-24T16:57:34.685198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-24T16:58:26.926287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-24T16:58:28.232784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-24T16:58:29.965941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-24T16:58:31.692458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-24T16:58:33.700333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-24T16:57:46.810059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-24T16:57:48.718411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-24T16:57:50.225906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-24T16:57:51.264071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

timetavgtmintmaxprcpsnowwdirwspdwpgtprestsun
01950-01-01-2.1-4.82.80.00.0NaNNaNNaN1031.4408.0
11950-01-020.6-5.72.813.20.0NaNNaNNaN1021.80.0
21950-01-032.51.04.80.80.0NaNNaNNaN1012.60.0
31950-01-043.91.45.33.40.0NaNNaNNaN1010.712.0
41950-01-055.31.77.11.00.0NaNNaNNaN1009.30.0
51950-01-065.02.78.30.00.0NaNNaNNaN1012.6222.0
61950-01-074.91.47.20.00.0NaNNaNNaN1019.36.0
71950-01-080.4-1.65.10.00.0NaNNaNNaN1021.8282.0
81950-01-091.6-2.13.70.00.0NaNNaNNaN1018.5126.0
91950-01-104.40.05.80.00.0NaNNaNNaN1026.90.0

Last rows

timetavgtmintmaxprcpsnowwdirwspdwpgtprestsun
262882021-12-22-3.0-6.71.70.00.0340.03.213.31025.8450.0
262892021-12-230.8-4.36.50.00.0149.012.629.21015.50.0
262902021-12-247.86.39.16.60.0164.09.425.21007.20.0
262912021-12-255.32.47.54.00.0356.06.518.71005.90.0
262922021-12-262.51.63.55.70.0311.05.012.61007.50.0
262932021-12-277.23.210.92.70.0150.06.822.31002.8122.0
262942021-12-289.17.711.26.90.0162.016.656.9998.75.0
262952021-12-299.78.611.54.00.0187.014.443.61009.04.0
262962021-12-3013.010.615.20.60.0169.011.528.11019.219.0
262972021-12-3111.49.313.40.00.0166.011.227.71023.3337.0